5 papers
DRBench: A Realistic Benchmark for Enterprise Deep Research
Amirhossein Abaskohi, Tianyi Chen, Miguel Muñoz-Mármol +11
We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior benchmarks that focus on simple questions…
Why 1 + 1 < 1 in Visual Token Pruning: Beyond Naive Integration via Multi-Objective Balanced Covering
Yangfu Li, Hongjian Zhan, Tianyi Chen +2
Existing visual token pruning methods target prompt alignment and visual preservation with static strategies, overlooking the varying relative importance of these objectives across…
Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
Shubham Gupta, Zichao Li, Tianyi Chen +4
Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale datasets. While effective, the standard practice…
Query Suggestion for Retrieval-Augmented Generation via Dynamic In-Context Learning
Fabian Spaeh, Tianyi Chen, Chen-Hao Chiang +1
Retrieval-augmented generation with tool-calling agents (agentic RAG) has become increasingly powerful in understanding, processing, and responding to user queries. However, the sc…
ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agents
Zhenyu Zhang, Tianyi Chen, Weiran Xu +2
Long-horizon tasks requiring multi-step reasoning and dynamic re-planning remain challenging for large language models (LLMs). Sequential prompting methods are prone to context dri…